Struc2mapGAN: improving synthetic cryo-EM density maps with generative adversarial networks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916637364453376 |
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| author | Zhang, Chenwei Condon, Anne Duc, Khanh Dao |
| author_facet | Zhang, Chenwei Condon, Anne Duc, Khanh Dao |
| contents | Generating synthetic cryogenic electron microscopy 3D density maps from molecular structures has potential important applications in structural biology. Yet existing simulation-based methods cannot mimic all the complex features present in experimental maps, such as secondary structure elements. As an alternative, we propose struc2mapGAN, a novel data-driven method that employs a generative adversarial network to produce improved experimental-like density maps from molecular structures. More specifically, struc2mapGAN uses a nested U-Net architecture as the generator, with an additional L1 loss term and further processing of raw training experimental maps to enhance learning efficiency. While struc2mapGAN can promptly generate maps after training, we demonstrate that it outperforms existing simulation-based methods for a wide array of tested maps and across various evaluation metrics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_17674 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Struc2mapGAN: improving synthetic cryo-EM density maps with generative adversarial networks Zhang, Chenwei Condon, Anne Duc, Khanh Dao Machine Learning Biomolecules Generating synthetic cryogenic electron microscopy 3D density maps from molecular structures has potential important applications in structural biology. Yet existing simulation-based methods cannot mimic all the complex features present in experimental maps, such as secondary structure elements. As an alternative, we propose struc2mapGAN, a novel data-driven method that employs a generative adversarial network to produce improved experimental-like density maps from molecular structures. More specifically, struc2mapGAN uses a nested U-Net architecture as the generator, with an additional L1 loss term and further processing of raw training experimental maps to enhance learning efficiency. While struc2mapGAN can promptly generate maps after training, we demonstrate that it outperforms existing simulation-based methods for a wide array of tested maps and across various evaluation metrics. |
| title | Struc2mapGAN: improving synthetic cryo-EM density maps with generative adversarial networks |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2407.17674 |